Ambareen Khan¹, Sher Afghan Khan², Mohammed Nishat Akhtar^1,*, Abdul Aabid^3,*, Muneer Baig³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 2007-2028, 2024, DOI:10.32604/fdmp.2024.049368 - 23 August 2024

Abstract When better fuel-air mixing in the combustion chamber or a reduction in base drag are required in vehicles, rockets, and aeroplanes, the base pressure control is activated. Controlling the base pressure and drag is necessary in both scenarios. In this work, semi-circular ribs with varying diameters (2, 4, and 6 mm) positioned at six distinct positions (0.5D, 1D, 1.5D, 2D, 3D, and 4D) inside a square duct with a side of 15 mm are proposed as an efficient way to apply the passive control technique. In-depth research is done on optimising rib size for various More > Graphic Abstract

Khaled Lotfy^1,2, A. M. S. Mahdy^3,*, Alaa A. El-Bary⁴, E. S. Elidy¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 107-126, 2024, DOI:10.32604/cmes.2024.053199 - 20 August 2024

Abstract In this research, we focus on the free-surface deformation of a one-dimensional elastic semiconductor medium as a function of magnetic field and moisture diffusivity. The problem aims to analyze the interconnection between plasma and moisture diffusivity processes, as well as thermo-elastic waves. The study examines the photo-thermoelasticity transport process while considering the impact of moisture diffusivity. By employing Laplace’s transformation technique, we derive the governing equations of the photo-thermo-elastic medium. These equations include the equations for carrier density, elastic waves, moisture transport, heat conduction, and constitutive relationships. Mechanical stresses, thermal conditions, and plasma boundary conditions More >

Wen Jiang^1,2, Mingshu Zhang^1,2,*, Xu'an Wang^1,3, Wei Bin^1,2, Xiong Zhang^1,2, Kelan Ren^1,2, Facheng Yan^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2161-2179, 2024, DOI:10.32604/cmc.2024.053762 - 15 August 2024

Abstract With the rapid spread of Internet information and the spread of fake news, the detection of fake news becomes more and more important. Traditional detection methods often rely on a single emotional or semantic feature to identify fake news, but these methods have limitations when dealing with news in specific domains. In order to solve the problem of weak feature correlation between data from different domains, a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed. This method makes full use of the attention mechanism, grasps the correlation between different… More >

Zhibo Yang, Xiaohan Huang, Bingdong Wang, Bin Hu, Zhenyong Zhang^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3001-3019, 2024, DOI:10.32604/cmc.2024.053369 - 15 August 2024

Abstract With the widespread use of machine learning (ML) technology, the operational efficiency and responsiveness of power grids have been significantly enhanced, allowing smart grids to achieve high levels of automation and intelligence. However, tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks, making it urgent to enhance their robustness. To address this, we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles. Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws, ensuring training data accurately reflects possible More >

Guodong Han, Hecheng Li^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2121-2137, 2024, DOI:10.32604/cmc.2024.053077 - 15 August 2024

Abstract Currently, there is a growing trend among users to store their data in the cloud. However, the cloud is vulnerable to persistent data corruption risks arising from equipment failures and hacker attacks. Additionally, when users perform file operations, the semantic integrity of the data can be compromised. Ensuring both data integrity and semantic correctness has become a critical issue that requires attention. We introduce a pioneering solution called Sec-Auditor, the first of its kind with the ability to verify data integrity and semantic correctness simultaneously, while maintaining a constant communication cost independent of the audited… More >

Jing Wang^1,2,*, Chen Zhang¹, Tianwen Lin¹

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1907-1925, 2024, DOI:10.32604/cmc.2024.052597 - 15 August 2024

Abstract When existing deep learning models are used for road extraction tasks from high-resolution images, they are easily affected by noise factors such as tree and building occlusion and complex backgrounds, resulting in incomplete road extraction and low accuracy. We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt. Then, ConvNeXt is used as the backbone network, which cooperates with the perceptual analysis network UPerNet, retains the detection head of the semantic segmentation, and builds a new model ConvNeXt-UPerNet to suppress noise interference. Training on the open-source DeepGlobe and CHN6-CUG… More >

Asifa Mehmood Qureshi¹, Abdul Haleem Butt¹, Abdulwahab Alazeb², Naif Al Mudawi², Mohammad Alonazi³, Nouf Abdullah Almujally⁴, Ahmad Jalal¹, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3315-3332, 2024, DOI:10.32604/cmc.2024.052582 - 15 August 2024

Abstract Intelligent vehicle tracking and detection are crucial tasks in the realm of highway management. However, vehicles come in a range of sizes, which is challenging to detect, affecting the traffic monitoring system’s overall accuracy. Deep learning is considered to be an efficient method for object detection in vision-based systems. In this paper, we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5 (YOLOv5) detector combined with a segmentation technique. The model consists of six steps. In the first step, all the extracted traffic sequence images are subjected… More >

Xiaoli Yuan, Jianxun Zhang^*, Xuejie Wang, Zhuhong Chu

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2443-2462, 2024, DOI:10.32604/cmc.2024.052285 - 15 August 2024

Abstract Semantic segmentation of driving scene images is crucial for autonomous driving. While deep learning technology has significantly improved daytime image semantic segmentation, nighttime images pose challenges due to factors like poor lighting and overexposure, making it difficult to recognize small objects. To address this, we propose an Image Adaptive Enhancement (IAEN) module comprising a parameter predictor (Edip), multiple image processing filters (Mdif), and a Detail Processing Module (DPM). Edip combines image processing filters to predict parameters like exposure and hue, optimizing image quality. We adopt a novel image encoder to enhance parameter prediction accuracy by More >

Jalil Abbas^1,*, Arshad Ahmad², Syed Muqsit Shaheed³, Rubia Fatima⁴, Sajid Shah⁵, Mohammad Elaffendi⁵, Gauhar Ali⁵

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2839-2855, 2024, DOI:10.32604/cmc.2024.052218 - 15 August 2024

Abstract The software development process mostly depends on accurately identifying both essential and optional features. Initially, user needs are typically expressed in free-form language, requiring significant time and human resources to translate these into clear functional and non-functional requirements. To address this challenge, various machine learning (ML) methods have been explored to automate the understanding of these requirements, aiming to reduce time and human effort. However, existing techniques often struggle with complex instructions and large-scale projects. In our study, we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier (FNRC). By combining the… More >

Donghui Ma¹, Wenqi Wu², Yuan Li¹, Lun Zhao¹, Yingchun Cai^2,*, Pan Guo^2,*, Shaolin Yang²

Structural Durability & Health Monitoring, Vol.18, No.5, pp. 663-681, 2024, DOI:10.32604/sdhm.2024.052677 - 19 July 2024

Abstract Segmentally assembled bridges are increasingly finding engineering applications in recent years due to their unique advantages, especially as urban viaducts. Vehicle loads are one of the most important variable loads acting on bridge structures. Accordingly, the influence of overloaded vehicles on existing assembled bridge structures is an urgent concern at present. This paper establishes the finite element model of the segmentally assembled bridge based on ABAQUS software and analyzes the influence of vehicle overload on an assembled girder bridge structure. First, a finite element model corresponding to the target bridge is established based on ABAQUS… More >